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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa reaching a Category 5 storm. While I am not ready to predict that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system drifts over very warm sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform standard meteorological experts at their specialty. Across all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to get ready for the disaster, potentially preserving lives and property.

The Way Google’s System Works

The AI system operates through spotting patterns that conventional lengthy scientific weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” he said.

Understanding AI Technology

It’s important to note, the system is an example of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for decades that can take hours to process and need some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Still, the reality that Google’s model could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of chance.”

He noted that while Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by offering additional internal information they can use to evaluate the reasons it is producing its conclusions.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the results of the model is kind of a black box,” said Franklin.

Broader Industry Developments

There has never been a private, for-profit company that has developed a top-level weather model which allows researchers a view of its techniques – in contrast to nearly all systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.